These da strategies combine the discriminator with a generative component, often based on gans. One of the typical cases is to use source images, noise vectors, or both, to generate simulated samples that are similar to the target ones, preserving the annotation information in the source domain and consequently tackling the problem of the lack of training data. A notable example is the generation of synthetic data with ground-truth annotations, which is helpful for training the target model.
For example, in [1]} a domain discriminator is employed to guarantee the invariance of content to the source domain, and a real/fake discriminator is paired with the generator to produce similar images to the target domain. More, the authors of [2]} introduced a method to improve the realism of synthetic images using unlabeled real data, combining an adversarial loss and a self-regularization loss. Instead, Bousmalis et al. [3]} introduced a novel model exploiting a gan conditioned by both a noise vector and source images; in this case, the classifier is trained to predict labels for both source and synthetic images, while the discriminator is trained to predict the domain labels belonging to the target and the synthetic images.
